Prosper Data by Snah Desai
## [1] "ListingKey"
## [2] "ListingNumber"
## [3] "ListingCreationDate"
## [4] "CreditGrade"
## [5] "Term"
## [6] "LoanStatus"
## [7] "ClosedDate"
## [8] "BorrowerAPR"
## [9] "BorrowerRate"
## [10] "LenderYield"
## [11] "EstimatedEffectiveYield"
## [12] "EstimatedLoss"
## [13] "EstimatedReturn"
## [14] "ProsperRating..numeric."
## [15] "ProsperRating..Alpha."
## [16] "ProsperScore"
## [17] "ListingCategory..numeric."
## [18] "BorrowerState"
## [19] "Occupation"
## [20] "EmploymentStatus"
## [21] "EmploymentStatusDuration"
## [22] "IsBorrowerHomeowner"
## [23] "CurrentlyInGroup"
## [24] "GroupKey"
## [25] "DateCreditPulled"
## [26] "CreditScoreRangeLower"
## [27] "CreditScoreRangeUpper"
## [28] "FirstRecordedCreditLine"
## [29] "CurrentCreditLines"
## [30] "OpenCreditLines"
## [31] "TotalCreditLinespast7years"
## [32] "OpenRevolvingAccounts"
## [33] "OpenRevolvingMonthlyPayment"
## [34] "InquiriesLast6Months"
## [35] "TotalInquiries"
## [36] "CurrentDelinquencies"
## [37] "AmountDelinquent"
## [38] "DelinquenciesLast7Years"
## [39] "PublicRecordsLast10Years"
## [40] "PublicRecordsLast12Months"
## [41] "RevolvingCreditBalance"
## [42] "BankcardUtilization"
## [43] "AvailableBankcardCredit"
## [44] "TotalTrades"
## [45] "TradesNeverDelinquent..percentage."
## [46] "TradesOpenedLast6Months"
## [47] "DebtToIncomeRatio"
## [48] "IncomeRange"
## [49] "IncomeVerifiable"
## [50] "StatedMonthlyIncome"
## [51] "LoanKey"
## [52] "TotalProsperLoans"
## [53] "TotalProsperPaymentsBilled"
## [54] "OnTimeProsperPayments"
## [55] "ProsperPaymentsLessThanOneMonthLate"
## [56] "ProsperPaymentsOneMonthPlusLate"
## [57] "ProsperPrincipalBorrowed"
## [58] "ProsperPrincipalOutstanding"
## [59] "ScorexChangeAtTimeOfListing"
## [60] "LoanCurrentDaysDelinquent"
## [61] "LoanFirstDefaultedCycleNumber"
## [62] "LoanMonthsSinceOrigination"
## [63] "LoanNumber"
## [64] "LoanOriginalAmount"
## [65] "LoanOriginationDate"
## [66] "LoanOriginationQuarter"
## [67] "MemberKey"
## [68] "MonthlyLoanPayment"
## [69] "LP_CustomerPayments"
## [70] "LP_CustomerPrincipalPayments"
## [71] "LP_InterestandFees"
## [72] "LP_ServiceFees"
## [73] "LP_CollectionFees"
## [74] "LP_GrossPrincipalLoss"
## [75] "LP_NetPrincipalLoss"
## [76] "LP_NonPrincipalRecoverypayments"
## [77] "PercentFunded"
## [78] "Recommendations"
## [79] "InvestmentFromFriendsCount"
## [80] "InvestmentFromFriendsAmount"
## [81] "Investors"Univariate Plots Section
There are 81 variables with 113937 observations.
## ListingKey ListingNumber
## 17A93590655669644DB4C06: 6 Min. : 4
## 349D3587495831350F0F648: 4 1st Qu.: 400919
## 47C1359638497431975670B: 4 Median : 600554
## 8474358854651984137201C: 4 Mean : 627886
## DE8535960513435199406CE: 4 3rd Qu.: 892634
## 04C13599434217079754AEE: 3 Max. :1255725
## (Other) :113912
## ListingCreationDate CreditGrade Term
## 2013-10-02 17:20:16.550000000: 6 :84984 Min. :12.00
## 2013-08-28 20:31:41.107000000: 4 C : 5649 1st Qu.:36.00
## 2013-09-08 09:27:44.853000000: 4 D : 5153 Median :36.00
## 2013-12-06 05:43:13.830000000: 4 B : 4389 Mean :40.83
## 2013-12-06 11:44:58.283000000: 4 AA : 3509 3rd Qu.:36.00
## 2013-08-21 07:25:22.360000000: 3 HR : 3508 Max. :60.00
## (Other) :113912 (Other): 6745
## LoanStatus ClosedDate
## Current :56576 :58848
## Completed :38074 2014-03-04 00:00:00: 105
## Chargedoff :11992 2014-02-19 00:00:00: 100
## Defaulted : 5018 2014-02-11 00:00:00: 92
## Past Due (1-15 days) : 806 2012-10-30 00:00:00: 81
## Past Due (31-60 days): 363 2013-02-26 00:00:00: 78
## (Other) : 1108 (Other) :54633
## BorrowerAPR BorrowerRate LenderYield
## Min. :0.00653 Min. :0.0000 Min. :-0.0100
## 1st Qu.:0.15629 1st Qu.:0.1340 1st Qu.: 0.1242
## Median :0.20976 Median :0.1840 Median : 0.1730
## Mean :0.21883 Mean :0.1928 Mean : 0.1827
## 3rd Qu.:0.28381 3rd Qu.:0.2500 3rd Qu.: 0.2400
## Max. :0.51229 Max. :0.4975 Max. : 0.4925
## NA's :25
## EstimatedEffectiveYield EstimatedLoss EstimatedReturn
## Min. :-0.183 Min. :0.005 Min. :-0.183
## 1st Qu.: 0.116 1st Qu.:0.042 1st Qu.: 0.074
## Median : 0.162 Median :0.072 Median : 0.092
## Mean : 0.169 Mean :0.080 Mean : 0.096
## 3rd Qu.: 0.224 3rd Qu.:0.112 3rd Qu.: 0.117
## Max. : 0.320 Max. :0.366 Max. : 0.284
## NA's :29084 NA's :29084 NA's :29084
## ProsperRating..numeric. ProsperRating..Alpha. ProsperScore
## Min. :1.000 :29084 Min. : 1.00
## 1st Qu.:3.000 C :18345 1st Qu.: 4.00
## Median :4.000 B :15581 Median : 6.00
## Mean :4.072 A :14551 Mean : 5.95
## 3rd Qu.:5.000 D :14274 3rd Qu.: 8.00
## Max. :7.000 E : 9795 Max. :11.00
## NA's :29084 (Other):12307 NA's :29084
## ListingCategory..numeric. BorrowerState
## Min. : 0.000 CA :14717
## 1st Qu.: 1.000 TX : 6842
## Median : 1.000 NY : 6729
## Mean : 2.774 FL : 6720
## 3rd Qu.: 3.000 IL : 5921
## Max. :20.000 : 5515
## (Other):67493
## Occupation EmploymentStatus
## Other :28617 Employed :67322
## Professional :13628 Full-time :26355
## Computer Programmer : 4478 Self-employed: 6134
## Executive : 4311 Not available: 5347
## Teacher : 3759 Other : 3806
## Administrative Assistant: 3688 : 2255
## (Other) :55456 (Other) : 2718
## EmploymentStatusDuration IsBorrowerHomeowner CurrentlyInGroup
## Min. : 0.00 False:56459 False:101218
## 1st Qu.: 26.00 True :57478 True : 12719
## Median : 67.00
## Mean : 96.07
## 3rd Qu.:137.00
## Max. :755.00
## NA's :7625
## GroupKey DateCreditPulled
## :100596 2013-12-23 09:38:12: 6
## 783C3371218786870A73D20: 1140 2013-11-21 09:09:41: 4
## 3D4D3366260257624AB272D: 916 2013-12-06 05:43:16: 4
## 6A3B336601725506917317E: 698 2014-01-14 20:17:49: 4
## FEF83377364176536637E50: 611 2014-02-09 12:14:41: 4
## C9643379247860156A00EC0: 342 2013-09-27 22:04:54: 3
## (Other) : 9634 (Other) :113912
## CreditScoreRangeLower CreditScoreRangeUpper
## Min. : 0.0 Min. : 19.0
## 1st Qu.:660.0 1st Qu.:679.0
## Median :680.0 Median :699.0
## Mean :685.6 Mean :704.6
## 3rd Qu.:720.0 3rd Qu.:739.0
## Max. :880.0 Max. :899.0
## NA's :591 NA's :591
## FirstRecordedCreditLine CurrentCreditLines OpenCreditLines
## : 697 Min. : 0.00 Min. : 0.00
## 1993-12-01 00:00:00: 185 1st Qu.: 7.00 1st Qu.: 6.00
## 1994-11-01 00:00:00: 178 Median :10.00 Median : 9.00
## 1995-11-01 00:00:00: 168 Mean :10.32 Mean : 9.26
## 1990-04-01 00:00:00: 161 3rd Qu.:13.00 3rd Qu.:12.00
## 1995-03-01 00:00:00: 159 Max. :59.00 Max. :54.00
## (Other) :112389 NA's :7604 NA's :7604
## TotalCreditLinespast7years OpenRevolvingAccounts
## Min. : 2.00 Min. : 0.00
## 1st Qu.: 17.00 1st Qu.: 4.00
## Median : 25.00 Median : 6.00
## Mean : 26.75 Mean : 6.97
## 3rd Qu.: 35.00 3rd Qu.: 9.00
## Max. :136.00 Max. :51.00
## NA's :697
## OpenRevolvingMonthlyPayment InquiriesLast6Months TotalInquiries
## Min. : 0.0 Min. : 0.000 Min. : 0.000
## 1st Qu.: 114.0 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 271.0 Median : 1.000 Median : 4.000
## Mean : 398.3 Mean : 1.435 Mean : 5.584
## 3rd Qu.: 525.0 3rd Qu.: 2.000 3rd Qu.: 7.000
## Max. :14985.0 Max. :105.000 Max. :379.000
## NA's :697 NA's :1159
## CurrentDelinquencies AmountDelinquent DelinquenciesLast7Years
## Min. : 0.0000 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 0.0 1st Qu.: 0.000
## Median : 0.0000 Median : 0.0 Median : 0.000
## Mean : 0.5921 Mean : 984.5 Mean : 4.155
## 3rd Qu.: 0.0000 3rd Qu.: 0.0 3rd Qu.: 3.000
## Max. :83.0000 Max. :463881.0 Max. :99.000
## NA's :697 NA's :7622 NA's :990
## PublicRecordsLast10Years PublicRecordsLast12Months RevolvingCreditBalance
## Min. : 0.0000 Min. : 0.000 Min. : 0
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 3121
## Median : 0.0000 Median : 0.000 Median : 8549
## Mean : 0.3126 Mean : 0.015 Mean : 17599
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 19521
## Max. :38.0000 Max. :20.000 Max. :1435667
## NA's :697 NA's :7604 NA's :7604
## BankcardUtilization AvailableBankcardCredit TotalTrades
## Min. :0.000 Min. : 0 Min. : 0.00
## 1st Qu.:0.310 1st Qu.: 880 1st Qu.: 15.00
## Median :0.600 Median : 4100 Median : 22.00
## Mean :0.561 Mean : 11210 Mean : 23.23
## 3rd Qu.:0.840 3rd Qu.: 13180 3rd Qu.: 30.00
## Max. :5.950 Max. :646285 Max. :126.00
## NA's :7604 NA's :7544 NA's :7544
## TradesNeverDelinquent..percentage. TradesOpenedLast6Months
## Min. :0.000 Min. : 0.000
## 1st Qu.:0.820 1st Qu.: 0.000
## Median :0.940 Median : 0.000
## Mean :0.886 Mean : 0.802
## 3rd Qu.:1.000 3rd Qu.: 1.000
## Max. :1.000 Max. :20.000
## NA's :7544 NA's :7544
## DebtToIncomeRatio IncomeRange IncomeVerifiable
## Min. : 0.000 $25,000-49,999:32192 False: 8669
## 1st Qu.: 0.140 $50,000-74,999:31050 True :105268
## Median : 0.220 $100,000+ :17337
## Mean : 0.276 $75,000-99,999:16916
## 3rd Qu.: 0.320 Not displayed : 7741
## Max. :10.010 $1-24,999 : 7274
## NA's :8554 (Other) : 1427
## StatedMonthlyIncome LoanKey TotalProsperLoans
## Min. : 0 CB1B37030986463208432A1: 6 Min. :0.00
## 1st Qu.: 3200 2DEE3698211017519D7333F: 4 1st Qu.:1.00
## Median : 4667 9F4B37043517554537C364C: 4 Median :1.00
## Mean : 5608 D895370150591392337ED6D: 4 Mean :1.42
## 3rd Qu.: 6825 E6FB37073953690388BC56D: 4 3rd Qu.:2.00
## Max. :1750003 0D8F37036734373301ED419: 3 Max. :8.00
## (Other) :113912 NA's :91852
## TotalProsperPaymentsBilled OnTimeProsperPayments
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 9.00 1st Qu.: 9.00
## Median : 16.00 Median : 15.00
## Mean : 22.93 Mean : 22.27
## 3rd Qu.: 33.00 3rd Qu.: 32.00
## Max. :141.00 Max. :141.00
## NA's :91852 NA's :91852
## ProsperPaymentsLessThanOneMonthLate ProsperPaymentsOneMonthPlusLate
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.00 Median : 0.00
## Mean : 0.61 Mean : 0.05
## 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :42.00 Max. :21.00
## NA's :91852 NA's :91852
## ProsperPrincipalBorrowed ProsperPrincipalOutstanding
## Min. : 0 Min. : 0
## 1st Qu.: 3500 1st Qu.: 0
## Median : 6000 Median : 1627
## Mean : 8472 Mean : 2930
## 3rd Qu.:11000 3rd Qu.: 4127
## Max. :72499 Max. :23451
## NA's :91852 NA's :91852
## ScorexChangeAtTimeOfListing LoanCurrentDaysDelinquent
## Min. :-209.00 Min. : 0.0
## 1st Qu.: -35.00 1st Qu.: 0.0
## Median : -3.00 Median : 0.0
## Mean : -3.22 Mean : 152.8
## 3rd Qu.: 25.00 3rd Qu.: 0.0
## Max. : 286.00 Max. :2704.0
## NA's :95009
## LoanFirstDefaultedCycleNumber LoanMonthsSinceOrigination LoanNumber
## Min. : 0.00 Min. : 0.0 Min. : 1
## 1st Qu.: 9.00 1st Qu.: 6.0 1st Qu.: 37332
## Median :14.00 Median : 21.0 Median : 68599
## Mean :16.27 Mean : 31.9 Mean : 69444
## 3rd Qu.:22.00 3rd Qu.: 65.0 3rd Qu.:101901
## Max. :44.00 Max. :100.0 Max. :136486
## NA's :96985
## LoanOriginalAmount LoanOriginationDate LoanOriginationQuarter
## Min. : 1000 2014-01-22 00:00:00: 491 Q4 2013:14450
## 1st Qu.: 4000 2013-11-13 00:00:00: 490 Q1 2014:12172
## Median : 6500 2014-02-19 00:00:00: 439 Q3 2013: 9180
## Mean : 8337 2013-10-16 00:00:00: 434 Q2 2013: 7099
## 3rd Qu.:12000 2014-01-28 00:00:00: 339 Q3 2012: 5632
## Max. :35000 2013-09-24 00:00:00: 316 Q2 2012: 5061
## (Other) :111428 (Other):60343
## MemberKey MonthlyLoanPayment LP_CustomerPayments
## 63CA34120866140639431C9: 9 Min. : 0.0 Min. : -2.35
## 16083364744933457E57FB9: 8 1st Qu.: 131.6 1st Qu.: 1005.76
## 3A2F3380477699707C81385: 8 Median : 217.7 Median : 2583.83
## 4D9C3403302047712AD0CDD: 8 Mean : 272.5 Mean : 4183.08
## 739C338135235294782AE75: 8 3rd Qu.: 371.6 3rd Qu.: 5548.40
## 7E1733653050264822FAA3D: 8 Max. :2251.5 Max. :40702.39
## (Other) :113888
## LP_CustomerPrincipalPayments LP_InterestandFees LP_ServiceFees
## Min. : 0.0 Min. : -2.35 Min. :-664.87
## 1st Qu.: 500.9 1st Qu.: 274.87 1st Qu.: -73.18
## Median : 1587.5 Median : 700.84 Median : -34.44
## Mean : 3105.5 Mean : 1077.54 Mean : -54.73
## 3rd Qu.: 4000.0 3rd Qu.: 1458.54 3rd Qu.: -13.92
## Max. :35000.0 Max. :15617.03 Max. : 32.06
##
## LP_CollectionFees LP_GrossPrincipalLoss LP_NetPrincipalLoss
## Min. :-9274.75 Min. : -94.2 Min. : -954.5
## 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.00 Median : 0.0 Median : 0.0
## Mean : -14.24 Mean : 700.4 Mean : 681.4
## 3rd Qu.: 0.00 3rd Qu.: 0.0 3rd Qu.: 0.0
## Max. : 0.00 Max. :25000.0 Max. :25000.0
##
## LP_NonPrincipalRecoverypayments PercentFunded Recommendations
## Min. : 0.00 Min. :0.7000 Min. : 0.00000
## 1st Qu.: 0.00 1st Qu.:1.0000 1st Qu.: 0.00000
## Median : 0.00 Median :1.0000 Median : 0.00000
## Mean : 25.14 Mean :0.9986 Mean : 0.04803
## 3rd Qu.: 0.00 3rd Qu.:1.0000 3rd Qu.: 0.00000
## Max. :21117.90 Max. :1.0125 Max. :39.00000
##
## InvestmentFromFriendsCount InvestmentFromFriendsAmount Investors
## Min. : 0.00000 Min. : 0.00 Min. : 1.00
## 1st Qu.: 0.00000 1st Qu.: 0.00 1st Qu.: 2.00
## Median : 0.00000 Median : 0.00 Median : 44.00
## Mean : 0.02346 Mean : 16.55 Mean : 80.48
## 3rd Qu.: 0.00000 3rd Qu.: 0.00 3rd Qu.: 115.00
## Max. :33.00000 Max. :25000.00 Max. :1189.00
## ## [1] 113937 81## 'data.frame': 113937 obs. of 81 variables:
## $ ListingKey : Factor w/ 113066 levels "00003546482094282EF90E5",..: 7180 7193 6647 6669 6686 6689 6699 6706 6687 6687 ...
## $ ListingNumber : int 193129 1209647 81716 658116 909464 1074836 750899 768193 1023355 1023355 ...
## $ ListingCreationDate : Factor w/ 113064 levels "2005-11-09 20:44:28.847000000",..: 14184 111894 6429 64760 85967 100310 72556 74019 97834 97834 ...
## $ CreditGrade : Factor w/ 9 levels "","A","AA","B",..: 5 1 8 1 1 1 1 1 1 1 ...
## $ Term : int 36 36 36 36 36 60 36 36 36 36 ...
## $ LoanStatus : Factor w/ 12 levels "Cancelled","Chargedoff",..: 3 4 3 4 4 4 4 4 4 4 ...
## $ ClosedDate : Factor w/ 2803 levels "","2005-11-25 00:00:00",..: 1138 1 1263 1 1 1 1 1 1 1 ...
## $ BorrowerAPR : num 0.165 0.12 0.283 0.125 0.246 ...
## $ BorrowerRate : num 0.158 0.092 0.275 0.0974 0.2085 ...
## $ LenderYield : num 0.138 0.082 0.24 0.0874 0.1985 ...
## $ EstimatedEffectiveYield : num NA 0.0796 NA 0.0849 0.1832 ...
## $ EstimatedLoss : num NA 0.0249 NA 0.0249 0.0925 ...
## $ EstimatedReturn : num NA 0.0547 NA 0.06 0.0907 ...
## $ ProsperRating..numeric. : int NA 6 NA 6 3 5 2 4 7 7 ...
## $ ProsperRating..Alpha. : Factor w/ 8 levels "","A","AA","B",..: 1 2 1 2 6 4 7 5 3 3 ...
## $ ProsperScore : num NA 7 NA 9 4 10 2 4 9 11 ...
## $ ListingCategory..numeric. : int 0 2 0 16 2 1 1 2 7 7 ...
## $ BorrowerState : Factor w/ 52 levels "","AK","AL","AR",..: 7 7 12 12 25 34 18 6 16 16 ...
## $ Occupation : Factor w/ 68 levels "","Accountant/CPA",..: 37 43 37 52 21 43 50 29 24 24 ...
## $ EmploymentStatus : Factor w/ 9 levels "","Employed",..: 9 2 4 2 2 2 2 2 2 2 ...
## $ EmploymentStatusDuration : int 2 44 NA 113 44 82 172 103 269 269 ...
## $ IsBorrowerHomeowner : Factor w/ 2 levels "False","True": 2 1 1 2 2 2 1 1 2 2 ...
## $ CurrentlyInGroup : Factor w/ 2 levels "False","True": 2 1 2 1 1 1 1 1 1 1 ...
## $ GroupKey : Factor w/ 707 levels "","00343376901312423168731",..: 1 1 335 1 1 1 1 1 1 1 ...
## $ DateCreditPulled : Factor w/ 112992 levels "2005-11-09 00:30:04.487000000",..: 14347 111883 6446 64724 85857 100382 72500 73937 97888 97888 ...
## $ CreditScoreRangeLower : int 640 680 480 800 680 740 680 700 820 820 ...
## $ CreditScoreRangeUpper : int 659 699 499 819 699 759 699 719 839 839 ...
## $ FirstRecordedCreditLine : Factor w/ 11586 levels "","1947-08-24 00:00:00",..: 8639 6617 8927 2247 9498 497 8265 7685 5543 5543 ...
## $ CurrentCreditLines : int 5 14 NA 5 19 21 10 6 17 17 ...
## $ OpenCreditLines : int 4 14 NA 5 19 17 7 6 16 16 ...
## $ TotalCreditLinespast7years : int 12 29 3 29 49 49 20 10 32 32 ...
## $ OpenRevolvingAccounts : int 1 13 0 7 6 13 6 5 12 12 ...
## $ OpenRevolvingMonthlyPayment : num 24 389 0 115 220 1410 214 101 219 219 ...
## $ InquiriesLast6Months : int 3 3 0 0 1 0 0 3 1 1 ...
## $ TotalInquiries : num 3 5 1 1 9 2 0 16 6 6 ...
## $ CurrentDelinquencies : int 2 0 1 4 0 0 0 0 0 0 ...
## $ AmountDelinquent : num 472 0 NA 10056 0 ...
## $ DelinquenciesLast7Years : int 4 0 0 14 0 0 0 0 0 0 ...
## $ PublicRecordsLast10Years : int 0 1 0 0 0 0 0 1 0 0 ...
## $ PublicRecordsLast12Months : int 0 0 NA 0 0 0 0 0 0 0 ...
## $ RevolvingCreditBalance : num 0 3989 NA 1444 6193 ...
## $ BankcardUtilization : num 0 0.21 NA 0.04 0.81 0.39 0.72 0.13 0.11 0.11 ...
## $ AvailableBankcardCredit : num 1500 10266 NA 30754 695 ...
## $ TotalTrades : num 11 29 NA 26 39 47 16 10 29 29 ...
## $ TradesNeverDelinquent..percentage. : num 0.81 1 NA 0.76 0.95 1 0.68 0.8 1 1 ...
## $ TradesOpenedLast6Months : num 0 2 NA 0 2 0 0 0 1 1 ...
## $ DebtToIncomeRatio : num 0.17 0.18 0.06 0.15 0.26 0.36 0.27 0.24 0.25 0.25 ...
## $ IncomeRange : Factor w/ 8 levels "$0","$1-24,999",..: 4 5 7 4 3 3 4 4 4 4 ...
## $ IncomeVerifiable : Factor w/ 2 levels "False","True": 2 2 2 2 2 2 2 2 2 2 ...
## $ StatedMonthlyIncome : num 3083 6125 2083 2875 9583 ...
## $ LoanKey : Factor w/ 113066 levels "00003683605746079487FF7",..: 100337 69837 46303 70776 71387 86505 91250 5425 908 908 ...
## $ TotalProsperLoans : int NA NA NA NA 1 NA NA NA NA NA ...
## $ TotalProsperPaymentsBilled : int NA NA NA NA 11 NA NA NA NA NA ...
## $ OnTimeProsperPayments : int NA NA NA NA 11 NA NA NA NA NA ...
## $ ProsperPaymentsLessThanOneMonthLate: int NA NA NA NA 0 NA NA NA NA NA ...
## $ ProsperPaymentsOneMonthPlusLate : int NA NA NA NA 0 NA NA NA NA NA ...
## $ ProsperPrincipalBorrowed : num NA NA NA NA 11000 NA NA NA NA NA ...
## $ ProsperPrincipalOutstanding : num NA NA NA NA 9948 ...
## $ ScorexChangeAtTimeOfListing : int NA NA NA NA NA NA NA NA NA NA ...
## $ LoanCurrentDaysDelinquent : int 0 0 0 0 0 0 0 0 0 0 ...
## $ LoanFirstDefaultedCycleNumber : int NA NA NA NA NA NA NA NA NA NA ...
## $ LoanMonthsSinceOrigination : int 78 0 86 16 6 3 11 10 3 3 ...
## $ LoanNumber : int 19141 134815 6466 77296 102670 123257 88353 90051 121268 121268 ...
## $ LoanOriginalAmount : int 9425 10000 3001 10000 15000 15000 3000 10000 10000 10000 ...
## $ LoanOriginationDate : Factor w/ 1873 levels "2005-11-15 00:00:00",..: 426 1866 260 1535 1757 1821 1649 1666 1813 1813 ...
## $ LoanOriginationQuarter : Factor w/ 33 levels "Q1 2006","Q1 2007",..: 18 8 2 32 24 33 16 16 33 33 ...
## $ MemberKey : Factor w/ 90831 levels "00003397697413387CAF966",..: 11071 10302 33781 54939 19465 48037 60448 40951 26129 26129 ...
## $ MonthlyLoanPayment : num 330 319 123 321 564 ...
## $ LP_CustomerPayments : num 11396 0 4187 5143 2820 ...
## $ LP_CustomerPrincipalPayments : num 9425 0 3001 4091 1563 ...
## $ LP_InterestandFees : num 1971 0 1186 1052 1257 ...
## $ LP_ServiceFees : num -133.2 0 -24.2 -108 -60.3 ...
## $ LP_CollectionFees : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LP_GrossPrincipalLoss : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LP_NetPrincipalLoss : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LP_NonPrincipalRecoverypayments : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PercentFunded : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Recommendations : int 0 0 0 0 0 0 0 0 0 0 ...
## $ InvestmentFromFriendsCount : int 0 0 0 0 0 0 0 0 0 0 ...
## $ InvestmentFromFriendsAmount : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Investors : int 258 1 41 158 20 1 1 1 1 1 ...| # Univariate Analysis |
| ### What is the structure of your dataset? Overall there are a total of 113937 observations on teh file and 86 variables. Out of that I observed a few factor variables such as Monthy Loan Payment, Debt to Income Ratio, Employment Status Duration, Prosper Score, Term, Borrower Rate, Borrower APR, Estimated Return, Stated Monthly Income and Lender Yeild ### What is/are the main feature(s) of interest in your dataset? I am looking at varibles that will affect the credit score of an individual looking to borrow. This will also dictate what rate and at what term the user will be able to borrow at through the service. ### What other features in the dataset do you think will help support your investigation into your feature(s) of interest? Monthly Income and Loan Payment, Interest Rates, Prosper Score and Term ### Did you create any new variables from existing variables in the dataset? Yes? ### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this? many graphs had their peaks to the right of the |
| # Univariate Section |
| The result shows the number of “NA” values in each column of the data |
r colSums(is.na(pf)) |
## ListingKey ListingNumber ## 0 0 ## ListingCreationDate CreditGrade ## 0 0 ## Term LoanStatus ## 0 0 ## ClosedDate BorrowerAPR ## 0 25 ## BorrowerRate LenderYield ## 0 0 ## EstimatedEffectiveYield EstimatedLoss ## 29084 29084 ## EstimatedReturn ProsperRating..numeric. ## 29084 29084 ## ProsperRating..Alpha. ProsperScore ## 0 29084 ## ListingCategory..numeric. BorrowerState ## 0 0 ## Occupation EmploymentStatus ## 0 0 ## EmploymentStatusDuration IsBorrowerHomeowner ## 7625 0 ## CurrentlyInGroup GroupKey ## 0 0 ## DateCreditPulled CreditScoreRangeLower ## 0 591 ## CreditScoreRangeUpper FirstRecordedCreditLine ## 591 0 ## CurrentCreditLines OpenCreditLines ## 7604 7604 ## TotalCreditLinespast7years OpenRevolvingAccounts ## 697 0 ## OpenRevolvingMonthlyPayment InquiriesLast6Months ## 0 697 ## TotalInquiries CurrentDelinquencies ## 1159 697 ## AmountDelinquent DelinquenciesLast7Years ## 7622 990 ## PublicRecordsLast10Years PublicRecordsLast12Months ## 697 7604 ## RevolvingCreditBalance BankcardUtilization ## 7604 7604 ## AvailableBankcardCredit TotalTrades ## 7544 7544 ## TradesNeverDelinquent..percentage. TradesOpenedLast6Months ## 7544 7544 ## DebtToIncomeRatio IncomeRange ## 8554 0 ## IncomeVerifiable StatedMonthlyIncome ## 0 0 ## LoanKey TotalProsperLoans ## 0 91852 ## TotalProsperPaymentsBilled OnTimeProsperPayments ## 91852 91852 ## ProsperPaymentsLessThanOneMonthLate ProsperPaymentsOneMonthPlusLate ## 91852 91852 ## ProsperPrincipalBorrowed ProsperPrincipalOutstanding ## 91852 91852 ## ScorexChangeAtTimeOfListing LoanCurrentDaysDelinquent ## 95009 0 ## LoanFirstDefaultedCycleNumber LoanMonthsSinceOrigination ## 96985 0 ## LoanNumber LoanOriginalAmount ## 0 0 ## LoanOriginationDate LoanOriginationQuarter ## 0 0 ## MemberKey MonthlyLoanPayment ## 0 0 ## LP_CustomerPayments LP_CustomerPrincipalPayments ## 0 0 ## LP_InterestandFees LP_ServiceFees ## 0 0 ## LP_CollectionFees LP_GrossPrincipalLoss ## 0 0 ## LP_NetPrincipalLoss LP_NonPrincipalRecoverypayments ## 0 0 ## PercentFunded Recommendations ## 0 0 ## InvestmentFromFriendsCount InvestmentFromFriendsAmount ## 0 0 ## Investors ## 0 |
| In the code bellow we find the most popular loan term legts whic in order are 36, 60 and 12 months. |
r pf$Term <- factor(pf$Term) table(pf$Term) |
## ## 12 36 60 ## 1614 87778 24545 |
r ggplot(aes(x=Term), data=pf) + geom_bar() |
 |
| Bellow is the histogram of Borrower Rates. The high peak is around .36 and the historgarm is right skewed. |
r qplot(BorrowerRate, data = pf, geom = "histogram", binwidth = .005) |
 |
| The most popular Borrower Rate is .3177 |
r pf %>% group_by(BorrowerRate) %>% summarise(Count=n()) %>% arrange(desc(Count)) |
## # A tibble: 2,294 × 2 ## BorrowerRate Count ## <dbl> <int> ## 1 0.3177 3672 ## 2 0.3500 1905 ## 3 0.3199 1651 ## 4 0.2900 1508 ## 5 0.2699 1319 ## 6 0.1500 1182 ## 7 0.1400 1035 ## 8 0.1099 949 ## 9 0.2000 907 ## 10 0.1585 806 ## # ... with 2,284 more rows |
| Bellow is the histogram for Borrower APR and we can see a peak at around .35 |
r qplot(BorrowerAPR, data = pf, geom = "histogram", binwidth = .001) |
## Warning: Removed 25 rows containing non-finite values (stat_bin). |
 |
| As it was visually shown in the histogram above and now a summary count bellow, the most popular APR is .35797 |
r pf %>% group_by(BorrowerAPR) %>% summarise(Count=n()) %>% arrange(desc(Count)) |
## # A tibble: 6,678 × 2 ## BorrowerAPR Count ## <dbl> <int> ## 1 0.35797 3672 ## 2 0.35643 1644 ## 3 0.37453 1260 ## 4 0.30532 902 ## 5 0.29510 747 ## 6 0.35356 721 ## 7 0.29776 707 ## 8 0.15833 652 ## 9 0.24246 605 ## 10 0.24758 601 ## # ... with 6,668 more rows |
| Histogram of Lender Yield |
r qplot(LenderYield, data = pf, geom = "histogram", binwidth = .001) |
 |
| Summary of yield (profit) of person lending/return on lending |
r pf %>% group_by(LenderYield) %>% summarise(Count=n()) %>% arrange(desc(Count)) |
## # A tibble: 2,283 × 2 ## LenderYield Count ## <dbl> <int> ## 1 0.3077 3672 ## 2 0.3400 1916 ## 3 0.3099 1651 ## 4 0.2599 1318 ## 5 0.1450 1011 ## 6 0.1300 999 ## 7 0.0999 955 ## 8 0.1400 877 ## 9 0.1485 801 ## 10 0.1199 779 ## # ... with 2,273 more rows |
| Average Lender Yield is .1827 |
r summary(pf$LenderYield) |
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -0.0100 0.1242 0.1730 0.1827 0.2400 0.4925 |
| Bellow is a histogram for Estimated Returns. As we can see the graph is skewed with outliers less than 0 and past .2 |
r qplot(EstimatedReturn, data = pf, geom = "histogram", binwidth = .001) |
## Warning: Removed 29084 rows containing non-finite values (stat_bin). |
 |
| The most common return is .14870 |
r pf %>% group_by(EstimatedReturn) %>% summarise(Count=n()) %>% arrange(desc(Count)) |
## # A tibble: 1,477 × 2 ## EstimatedReturn Count ## <dbl> <int> ## 1 NA 29084 ## 2 0.12460 2217 ## 3 0.14870 1097 ## 4 0.06910 760 ## 5 0.14140 738 ## 6 0.10740 654 ## 7 0.11500 611 ## 8 0.07713 565 ## 9 0.06706 556 ## 10 0.09050 538 ## # ... with 1,467 more rows |
| The average estimated return .096 |
r summary(pf$EstimatedReturn) |
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## -0.183 0.074 0.092 0.096 0.117 0.284 29084 |
| The graph bellow is right skewed without a peak at the right. |
r ggplot(aes(x = EmploymentStatusDuration), data=pf) + geom_histogram(bins=100) |
## Warning: Removed 7625 rows containing non-finite values (stat_bin). |
 |
| Bellow is a histogram of loan ammounts issued showing a right skewed graph. |
r ggplot(aes(x = LoanOriginalAmount), data = pf) + geom_histogram(bins=10) |
 |
| Prosper scores range from 1 to 11. 1 being the highest risk and 11 being lowest. The bar graph shows the distribution of scores. |
r ggplot(aes(x = ProsperScore), data = pf) + geom_bar() |
## Warning: Removed 29084 rows containing non-finite values (stat_count). |
 |
| The table shows how many people hold each score |
r table(pf$ProsperScore) |
## ## 1 2 3 4 5 6 7 8 9 10 11 ## 992 5766 7642 12595 9813 12278 10597 12053 6911 4750 1456 |
| Bellow is the histogram of length in months for Employment Status Duration. We can see that after the sqrt is applied there is a clear right skew |
r ggplot(aes(x = EmploymentStatusDuration), data = pf) + geom_histogram(bins=100) |
## Warning: Removed 7625 rows containing non-finite values (stat_bin). |
|
r ggplot(aes(x = EmploymentStatusDuration), data = pf) + geom_histogram(bins=100) + scale_x_sqrt() |
## Warning: Removed 7625 rows containing non-finite values (stat_bin). |
 |
| Bellow is a summary of the length in months of the employment status at the time the listing was created. |
r summary(pf$EmploymentStatusDuration) |
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 0.00 26.00 67.00 96.07 137.00 755.00 7625 |
| Bellow is a summary of the Debt to Income Ratios for borrowers. |
r summary(pf$DebtToIncomeRatio) |
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 0.000 0.140 0.220 0.276 0.320 10.010 8554 |
| Bellow is the debt to income ratio of the borrower at the time the credit profile was pulled. This value is Null if the debt to income ratio is not available. This value is capped at 10.01 (any debt to income ratio larger than 1000% will be returned as 1001%). |
r ggplot(aes(x = DebtToIncomeRatio), data=subset(pf,!is.na(DebtToIncomeRatio))) + geom_histogram(bins=100) |
 |
| Table view of the Debt to Income Ratio’s |
r pf %>% group_by(DebtToIncomeRatio) %>% summarise(Count=n()) %>% arrange(desc(Count)) |
## # A tibble: 1,208 × 2 ## DebtToIncomeRatio Count ## <dbl> <int> ## 1 NA 8554 ## 2 0.18 4132 ## 3 0.22 3687 ## 4 0.17 3616 ## 5 0.14 3553 ## 6 0.20 3481 ## 7 0.16 3442 ## 8 0.19 3392 ## 9 0.15 3338 ## 10 0.21 3226 ## # ... with 1,198 more rows |
| Sumary of the Monthly Loan Payments by borrowers |
r summary(pf$MonthlyLoanPayment) |
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.0 131.6 217.7 272.5 371.6 2252.0 |
| Histogram of Monthly Loan Payments |
r ggplot(aes(x = MonthlyLoanPayment), data = pf) + geom_histogram(bins=100) |
 |
| Instead of using Credit Score Range Lower and Credit Score Range Upper I combined the two with average and created the Credit Score Range Mid. This is what I used to plot the histogram of Credit Scores. |
| ```r pf\(CSRangeMid <- (pf\)CreditScoreRangeLower + pf$CreditScoreRangeUpper) /2 |
| ggplot(aes(x = pf$CSRangeMid), data = pf) + geom_histogram(bins=100) ``` |
## Warning: Removed 591 rows containing non-finite values (stat_bin). |
 |
r summary(pf$CreditScoreRangeLower) |
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 0.0 660.0 680.0 685.6 720.0 880.0 591 |
| The histogram of StatedMonthlyIncome is a bit right skewed mainly due to some outliers as outlined in the summary. |
| After applying log10 to the y-axis the graph better shows the right skew. |
| Summary of StatedMonthlyIncome shows a big difference between 3rd quadrant and the maximum value thus showing there are large outliers in the data. |
r qplot(data = pf, x = StatedMonthlyIncome) + xlim(c(0, 50000)) |
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. |
## Warning: Removed 83 rows containing non-finite values (stat_bin). |
 |
r ggplot(aes(x = StatedMonthlyIncome), data=subset(pf, StatedMonthlyIncome < 50000)) + geom_histogram(bins = 100) + scale_y_log10() |
## Warning: Transformation introduced infinite values in continuous y-axis |
## Warning: Removed 3 rows containing missing values (geom_bar). |
 |
r summary(pf$StatedMonthlyIncome) |
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3200 4667 5608 6825 1750000 |
| The table shows that there are 1140 people with monthly income greater than 20526.67. Most of the people’s income is around at 4667. |
r pf %>% group_by(StatedMonthlyIncome) %>% summarise(Count=n()) %>% arrange(desc(Count)) |
## # A tibble: 13,502 × 2 ## StatedMonthlyIncome Count ## <dbl> <int> ## 1 4166.667 3526 ## 2 5000.000 3389 ## 3 3333.333 2917 ## 4 3750.000 2428 ## 5 5416.667 2374 ## 6 5833.333 2319 ## 7 6250.000 2276 ## 8 2500.000 2256 ## 9 4583.333 2211 ## 10 6666.667 2162 ## # ... with 13,492 more rows |
Bivariate Analysis
Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?
What would seem like an obvious observation would be the relationship between Prosper Score and Borrower Rate. With the bellow Jitter plot you can see that as Prosper Scores increase the Borrower Rate decreases. This same concept applies to Credit Scores, we can see that as an individuals credit score inceases there is a general trend that their Borrower Rate will decease.
A key indicaiton I came across was that as prosper scores increase an individuals Credit Score also increases and that means they will not only be able to borrow at a better rate but also have a larger potential loan ammount.
Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?
As Borrower Rate increased so did the Estimated Return. People with greater income have better credit scores thus them being able to increase their loan payment and overall amount.
What was the strongest relationship you found?
Monthly Loan Payment and Loan Amount and Borrower Rate with Prosper Score.
Bivariate Plots Section
From the jitter plot we ecan see that the lower the Prosper Score the higher the Borrower Rate is.
## Warning: Removed 29084 rows containing missing values (geom_point).
ggplot(aes(factor(ProsperScore), BorrowerRate), data = pf) +
geom_jitter( alpha = .05) +
geom_boxplot( alpha = .5,color = 'blue')+
stat_summary(fun.y = "mean",
geom = "point",
color = "red",
shape = 8,
size = 4)+
geom_smooth(aes(ProsperScore,
BorrowerRate),
method = "lm",
se = FALSE,size=2)## Warning: Removed 29084 rows containing non-finite values (stat_smooth).
Bellow is a scatter plot maping Borrower Rate against Estimated Return. We can see that BorrowerRate and Estimated return are positively correlated.
ggplot(aes(y = BorrowerRate,x = EstimatedReturn), data = pf) +
geom_jitter(alpha=0.01,size=2) ## Warning: Removed 29084 rows containing missing values (geom_point).
From the jitter plot bellow we can see that Credit Score and BorrowerRate are negatively correlated.
ggplot(aes(y = BorrowerRate,x = CSRangeMid),
data = subset(pf,CSRangeMid>300)) +
geom_jitter(alpha=0.01,size=2)
Individuals with the best Credit Scores have a Posper score of 10 and there is a drop off before and after.
ggplot(aes(y = CSRangeMid,x = ProsperScore), data = pf) +
geom_jitter(alpha=0.01,size=2)## Warning: Removed 29084 rows containing missing values (geom_point).
summary(pf$CreditScoreRangeUpper)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 19.0 679.0 699.0 704.6 739.0 899.0 591We can observe a positive relationship between Loan Amount and Monthly Payment. As the Loan Amount increases, so does the Monthly Payments.
ggplot(aes(x = LoanOriginalAmount,
y = MonthlyLoanPayment , color = factor(ProsperScore)),
data = pf) +
geom_point(alpha = 0.8, size = 2) +
geom_smooth(method = "lm", se = FALSE,size=1) +
scale_color_brewer(type='seq',
guide=guide_legend(title='ProsperScore'))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Blues is 9
## Returning the palette you asked for with that many colors## Warning: Removed 35290 rows containing missing values (geom_point).
Individuals that Completed their loans people have greater incomes than Defaulted people.
by(pf$StatedMonthlyIncome, pf$LoanStatus, summary)## pf$LoanStatus: Cancelled
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 2445 2600 2609 3833 4167
## --------------------------------------------------------
## pf$LoanStatus: Chargedoff
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 2500 3750 4486 5500 208300
## --------------------------------------------------------
## pf$LoanStatus: Completed
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 2917 4417 5325 6583 618500
## --------------------------------------------------------
## pf$LoanStatus: Current
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3667 5167 6153 7447 1750000
## --------------------------------------------------------
## pf$LoanStatus: Defaulted
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 2500 3708 4367 5417 58620
## --------------------------------------------------------
## pf$LoanStatus: FinalPaymentInProgress
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1167 3583 5250 6312 8333 32920
## --------------------------------------------------------
## pf$LoanStatus: Past Due (>120 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3115 3750 3727 4500 6667
## --------------------------------------------------------
## pf$LoanStatus: Past Due (1-15 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3167 4667 5554 6948 35420
## --------------------------------------------------------
## pf$LoanStatus: Past Due (16-30 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3250 4583 5484 6500 30000
## --------------------------------------------------------
## pf$LoanStatus: Past Due (31-60 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 2938 4583 5436 7083 25000
## --------------------------------------------------------
## pf$LoanStatus: Past Due (61-90 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3167 4583 5323 6594 31250
## --------------------------------------------------------
## pf$LoanStatus: Past Due (91-120 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3073 4171 4816 5833 22920People with lower Borrower Rates complete their loan payments better than those with higher rates.
by(pf$BorrowerRate, pf$LoanStatus, summary)## pf$LoanStatus: Cancelled
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1075 0.1395 0.2000 0.1844 0.2375 0.2375
## --------------------------------------------------------
## pf$LoanStatus: Chargedoff
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0100 0.1769 0.2400 0.2354 0.2975 0.4500
## --------------------------------------------------------
## pf$LoanStatus: Completed
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.1173 0.1744 0.1864 0.2511 0.4975
## --------------------------------------------------------
## pf$LoanStatus: Current
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0577 0.1314 0.1760 0.1838 0.2310 0.3304
## --------------------------------------------------------
## pf$LoanStatus: Defaulted
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.1650 0.2296 0.2231 0.2875 0.4975
## --------------------------------------------------------
## pf$LoanStatus: FinalPaymentInProgress
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0629 0.1299 0.1899 0.1970 0.2712 0.3199
## --------------------------------------------------------
## pf$LoanStatus: Past Due (>120 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1449 0.2079 0.2551 0.2527 0.3060 0.3199
## --------------------------------------------------------
## pf$LoanStatus: Past Due (1-15 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0749 0.1870 0.2317 0.2308 0.2859 0.3435
## --------------------------------------------------------
## pf$LoanStatus: Past Due (16-30 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0599 0.1899 0.2419 0.2353 0.2909 0.3304
## --------------------------------------------------------
## pf$LoanStatus: Past Due (31-60 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0649 0.1855 0.2468 0.2330 0.2870 0.3304
## --------------------------------------------------------
## pf$LoanStatus: Past Due (61-90 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0659 0.1914 0.2468 0.2400 0.2999 0.3304
## --------------------------------------------------------
## pf$LoanStatus: Past Due (91-120 days)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0766 0.1850 0.2495 0.2383 0.2952 0.3435From the jitter pot bellow we can see that generally people with good credit score have greater loan amounts.
ggplot(aes(x = CSRangeMid,y = LoanOriginalAmount),
data = subset(pf,CSRangeMid > 350)) +
geom_jitter(alpha = .05, size = 2)
Multivariate Analysis
Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
First, People with higher Prosper Scores tend to pay back their loans quicker. I Things such as the individuals monthly income, credit score, loan amount and periodic loan payment are contributing factors Prosper Score but there is no strong relation between Prosper Score Borrower Rate. ### Were there any interesting or surprising interactions between features? Interestingly enough, most people fulfilled their loan payments. Another interesting thing I found was that people paid their loans with 0 reported income. ### OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.
Multivariate Plots Section
Bellow is the jitter plot between Loan Original Amount and Monthly Loan Payment colored by Prosper Score
An interesting finding to note is that most of the people who are currently paying their loans are self-employed.
ggplot(aes(y = BorrowerRate, x = ProsperScore, color = EmploymentStatus),
data=subset(pf,EmploymentStatus != "" &
EmploymentStatus != "Employed" &
EmploymentStatus != "Other" &
!is.na(ProsperScore))) +
geom_jitter(alpha=1, size=2) +
scale_color_brewer(type='qual')
Similiar to the graph above the one bellow demostrates the relationship between Employment Status, Borrower Rate and the Prosper score but in the form of a Box Plot.
ggplot(aes(y = BorrowerRate, x = factor(ProsperScore), fill = EmploymentStatus),
data=subset(pf,EmploymentStatus != "" &
EmploymentStatus != "Employed" &
EmploymentStatus != "Other" &
!is.na(ProsperScore))) +
geom_boxplot( ) +
scale_color_brewer(type='qual')
Bellow is a jitter plot with Montly Loan Payment and Credit Score Range Mid colored by Prosper Score.
ggplot(aes(x = MonthlyLoanPayment, y = CSRangeMid,
color = ProsperScore), data = pf) +
geom_jitter(alpha=0.5, size=2) ## Warning: Removed 591 rows containing missing values (geom_point).
Final Plots and Summary
Plot One
Description One
This graph shows ProsperScore and BorrowerRate. The graph shows that Borrower Rate decrease as the ProsperScore increases. This graph is to show a clear relationship that prosper score does affect ones ability to borrow and more then that at what rate they can borrow. As this is a major factor in someones loan consideration, its key to establish a realtionship and then look further into what goes into this.
Plot Two
Description Two
Above is a histogram of ProsperScores and it has a normal distribution. As we can see most scores are between 4 and 8. This histogram shows the distrubtion of scores and will lead to an indicator for further analysis and validation of what affects the prosper and credit scores.
Plot Three
Description Three
The graph shows that people with better Prosper Scores and Credit Scores have larger stated montly incomes and the opposite applies. As Credit Score inceases so does the Stated Monthly Income. This was done to understand that while an individuals Credit Scores goes into defining their Prosper Score, does this also mean that there is a change in their incomes. As it relates to my iniditual question I want to understand the factors that ultimetly go into an individuals Credit Score and Propser Score and one of the biggest factors that is many times assumed is Income.
Reflection
With a total of 113937 data points and 81 variables I first removed “NA” Variables as they may get in the way of my analysis. To rarget some points I was interested in, specifically how Prosper Score is found and affects to Credit Score I looked at variables such as MonthlIncome, CreditScore, BorrowerRate, BorrowerAPR and ProsperScore. There was also outliers in the MonthlyIncome variable.
Some interesting things I found during analysis was that people who have good ProsperScores tends to have a lower BorrowerRate. This same concept applies to Credit Scores, we can see that as an individuals credit score inceases there is a general trend that their Borrower Rate will decease. I aso observed that as someones prosper scores increase an individuals Credit Score also increases and that means they will not only be able to borrow at a better rate but also have a larger potential loan ammount.
All said and done the varibles that affect ProsperScores are the same as which they affect the CreditScore and from there those same variables affect an individuals BorrowRate. Especially as an individuals Income and pre exisitng credit scores do hav an affect on the end borrower rate, Prosper Score and other things such as loan ammount and term.
There are a great deal of interesting variables to be explored, cleaned and interipuriated. That being said, there was a lot that I was unable to explore such as what areas had the most amount of loans issues as well as their amounts. I would also be interested in seeing what loan amount had the highest percentage of completing the loan that would also be combiend with looking into what type of loan has the highest percentage of completion.